Revisiting 1998 torque data: a machine learning analysis of time series data for tapping experiments
Abstract
This paper addresses the challenge of real-time tool condition monitoring in tapping processes using machine learning techniques, with a focus on cross-material generalization and robust fault detection. The study leverages a historical dataset from 1988, comprising 2,195 tapping experiments on two steel alloys - 16 MnCr 5 and 42 CrMo 4 - monitoring torque (Mz) signals to predict binary quality outcomes (good/bad) based on defined quality criteria. To overcome limitations in prior work, the authors introduce a feature extraction method that captures both amplitude and duration characteristics across distinct phases of the torque signal,. The evaluation framework includes increasingly challenging train/test splits: random, run-wise holdout, and cross-material (training on 16 MnCr 5, testing on 42 CrMo 4), enabling assessment of real-world generalizability.
Multiple machine learning models are tested using both raw time-series data (after cleaning and normalization) and engineered features. Results show Matthews Correlation Coefficients (MCC) of 0.40 - 0.41 under cross-material testing-indicating moderate but meaningful generalization across materials with different mechanical properties. This performance level suggests that fundamental physical regularities in successful tapping produce consistent torque signatures, enabling transferable detection of anomalies without retraining. Findings support the feasibility of plug-and-play monitoring systems in agile manufacturing environments, where minimal setup and broad applicability are essential.
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| Titel | Revisiting 1998 torque data: a machine learning analysis of time series data for tapping experiments |
|---|---|
| Medien | 2026 IEEE International Conference on Advanced Systems and Emergent Technologies (ic_aset) |
| Band | 2026 |
| Verfasser | Alaa Al Najjar, Nakul Halvadar, Prof. Dr.-Ing. Valentin Plenk, Prof. Dr.-Ing. Marco Linß |
| Veröffentlichungsdatum | 11.05.2026 |
| Zitation | Al Najjar, Alaa; Halvadar, Nakul; Plenk, Valentin; Linß, Marco (2026): Revisiting 1998 torque data: a machine learning analysis of time series data for tapping experiments. 2026 IEEE International Conference on Advanced Systems and Emergent Technologies (ic_aset) 2026. DOI: 10.1109/IC_ASET69920.2026.11502539 |